KR101381130B1 - Multi-user precoding and scheduling method and base station for implementing the method - Google Patents

Multi-user precoding and scheduling method and base station for implementing the method Download PDF

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KR101381130B1
KR101381130B1 KR1020107006736A KR20107006736A KR101381130B1 KR 101381130 B1 KR101381130 B1 KR 101381130B1 KR 1020107006736 A KR1020107006736 A KR 1020107006736A KR 20107006736 A KR20107006736 A KR 20107006736A KR 101381130 B1 KR101381130 B1 KR 101381130B1
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user
csi
scheduling
error
statistical
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KR20100057879A (en
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킹 유
레이 왕
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알까뗄 루슨트
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413MIMO systems
    • H04B7/0456Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413MIMO systems
    • H04B7/0417Feedback systems
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413MIMO systems
    • H04B7/0452Multi-user MIMO systems
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621Feedback content
    • H04B7/0626Channel coefficients, e.g. channel state information [CSI]
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0652Feedback error handling
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W72/00Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
    • H04W72/12Dynamic Wireless traffic scheduling ; Dynamically scheduled allocation on shared channel
    • H04W72/1205Schedule definition, set-up or creation
    • H04W72/1226Schedule definition, set-up or creation based on channel quality criteria, e.g. channel state dependent scheduling
    • H04W72/1231Schedule definition, set-up or creation based on channel quality criteria, e.g. channel state dependent scheduling using measured or perceived quality
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W88/00Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
    • H04W88/08Access point devices

Abstract

A multi-user precoding and scheduling method comprising the steps of: feeding back channel state information (CSI) and statistical characteristics of CSI errors (101) from a user equipment (UE) to a base station (BS); Generating (103) at the BS a multi-user precoding matrix and scheduling scheme in accordance with the statistical characteristics of the fed back CSI and CSI error; And performing 105 multi-user precoding and scheduling on user data by using the generated multi-user precoding matrix and scheduling scheme. The base station is used to implement the method.

Description

MULTI-USER PRECODING AND SCHEDULING METHOD AND BASE STATION FOR IMPLEMENTING THE METHOD}

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a wireless multiple input multiple output (MIMO) communication system, and more particularly, to statistics of channel state information (CSI that may be inaccurate due to various factors such as feedback quantization, feedback delay, etc.) and CSI errors fed back from the user side. A multi-user (MU) precoding and scheduling method capable of generating a multi-user precoding matrix and scheduling scheme for multi-user precoding and scheduling by using features.

Recently, MU-MIMO has attracted much attention due to its advantages in capacity as well as the ability to operate for single antenna users (SU) while maintaining MIMO benefits.

Compared with SU-MIMO, the transmission process of MU-MIMO is complicated by the fact that each user must decode their messages independently without joint operation with other users. The key problem is how to resolve co-channel interference (CCI) among users.

To solve this problem, multi-user precoding techniques are used in the MU-MIMO system to control or completely avoid CCI between users so that each user receives no or only limited interference from other users. For full avoidance or effective control of CCI, full CSI for all users is required at the transmitter, which is just an unrealistic assumption for real systems. In practice, full CSI is difficult to achieve, so incomplete CSI is always used at the transmitter. With such incomplete CSI, CCI among users cannot be completely avoided even when using zero-forcing type precoding algorithms. Residual CCI due to incomplete CSI cannot be suppressed at the receiver by conventional interference-suppression methods such as maximum-likelihood (ML) or minimum-mean-squared-error (MMSE) detection. After all, CCI can only be regarded as additive noise, in which the average power increases with increasing total transmit power. This feature of CCI significantly limits the performance of MU-MIMO, especially at high SNRs.

In order to solve the degradation of MU-MIMO performance caused by residual CCI due to incomplete CSI at the transmitter, the present invention provides a CSI information fed back from a user by a multi-user precoding matrix and scheduling scheme for multi-user precoding and scheduling. And a method of multi-user precoding and scheduling generated by using statistical characteristics of CSI errors. The resulting precoding matrix and scheduling scheme can adapt to situations where channel information is incomplete at the transmitter, reduce residual CCI and reduce degradation of MU-MIMO performance due to incomplete CSI.

SUMMARY OF THE INVENTION An object of the present invention is a multi-user precoding and scheduling method comprising the steps of: feeding back channel state information (CSI) and statistical characteristics of CSI errors from a user equipment (UE) to a base station (BS); Generating, at the BS, a multi-user precoding matrix and scheduling scheme in accordance with the statistical characteristics of the fed back CSI and CSI error; And performing multi-user precoding and scheduling on user data by using the generated multi-user precoding matrix and scheduling scheme.

Preferably, the CSI is an estimate of the channel matrix.

Advantageously, said statistical characteristics of said CSI error comprise a covariance matrix of errors of said estimate of said channel matrix.

Advantageously, said multi-user precoding uses a minimum mean square error (MMSE) type algorithm.

Preferably, the MMSE type algorithm is a continuous MMSE algorithm.

Advantageously, said multi-user scheduling uses capacity maximization criteria.

Advantageously, the statistical characteristics of the CSI error are obtained by measuring channel estimate error, feedback error and quantization error.

Preferably, the method is used in a MU-MIMO communication system.

The present invention also provides a base station comprising: a receiving device for receiving CSI and statistical characteristics of a CSI error fed back from a user equipment (UE); A multi-user precoding matrix and scheduling scheme generation device for generating a multi-user precoding matrix and a scheduling scheme in accordance with the fed back CSI and CSI error statistical characteristics; And a multi-user precoding and scheduling device for performing multi-user precoding and scheduling on user data by using the generated multi-user precoding matrix and scheduling scheme.

The objects, advantages and features of the present invention will become more apparent according to the description of the preferred embodiments with reference to the drawings.

 The precoding matrix and scheduling scheme generated in accordance with the present invention can adapt to situations where channel information is incomplete at the transmitter, reduce residual CCI and reduce degradation of MU-MIMO performance due to incomplete CSI.

1 is a flow diagram of a method of multi-user precoding and scheduling in accordance with the present invention.
2 is a block diagram of a BS for implementing the method of multi-user precoding and scheduling in accordance with the present invention.
3 and 4 show graphs of performance comparison between the method according to the invention and the prior art method.

With incomplete CSI, the transmitter cannot generate a multi-user precoding matrix that exactly matches the multi-user channel. This mismatching leads to additional CCI among users, which increases with transmit power and severely restricts the performance of MU-MIMO, especially for high SNRs. The CCI caused by the CSI error cannot be suppressed at the receiver by common interference-suppression methods such as maximum likelihood (ML) and MMSE detection. The effect is similar to the increase in additive noise. The basic idea of the present invention is to investigate the relationship between this CCI level and the precoding / scheduling results and use this relationship to adjust the precoding and scheduling algorithms to better suit the incomplete CSI environment. In accordance with the present invention, a method of multi-user precoding and scheduling is provided to better control CCI levels due to CSI errors and to improve MU-MIMO performance with incomplete CSI at the transmitter.

In the present invention, it is assumed that statistical characteristics of CSI error can be used at the transmitter. In multi-user precoding, this algorithm is commonly used due to the ability of the MMSE type algorithm to balance interference and noise. Here, the CCI caused by the CSI error can be regarded as AWGN (Additive White Gaussian Noise), and the relationship between the average power of the CSI error and the precoding / filtering matrix can be established based on the dispersion matrix of the CSI error. have. The CCI level, precoding and filtering matrices are then jointly optimized according to the MMSE criteria.

In the present invention, for multi-user scheduling, the capacity-maximization criterion is adopted and the user / mode subset with the maximum achievable sum capacity is selected. Similarly, by considering CCI as AWGN when estimating the sum capacity for each user / mode subset, a relationship can be established and used between the CCI level related to CSI error and user / mode selection to adjust the scheduling operation. .

Hereinafter, the principle of the method of multi-user precoding and scheduling according to the present invention will be described in detail.

Channel model

Consider a downlink of a multi-user MIMO system with K users receiving services from the same BS with the same time-frequency resource with N T transmit antennas and each with N R receive antennas at the BS. (Note that K is the number of users served by the same time-slot and frequency band by spatial processing. The total number of users in a cell may be much larger than K).

Assuming frequency flat fading for all users, the channel matrix for user k is

Figure 112013023965418-pct00001
, ≪ / RTI >
Figure 112013023965418-pct00002
Is the fading coefficient between the nth transmit antenna and the mth receive antenna of user k. The number of data streams dedicated to user k is denoted by s k .
Figure 112013023965418-pct00003
And
Figure 112013023965418-pct00004
It is always assumed. First, the user vector k is linearly transformed into a symbol vector of length N T for transmission from the N T antennas by multiplying the data vector x k of length s k by the N T xs k precoding matrix T k . The length N T symbol vectors for K users are linearly superimposed and transmitted simultaneously from the antenna array to the channel. Here, it is always assumed that the elements of x k are independent and equally distributed with zero mean and unit deviation. The total transmit power is then given by

Figure 112010019458035-pct00005

For each user k, the received signal vector is

Figure 112010019458035-pct00006

Where n k is the zero mean

Figure 112010019458035-pct00007
Is a vector of samples of an AWGN process with a variance of. Each user k is estimated for x k by multiplying y k by s k x N R filtering matrix B k , as given by
Figure 112010019458035-pct00008
.

Figure 112010019458035-pct00009

In equation (3), the filtering matrix B k can be derived based on various criteria such as MMSE. Based on equation (3),

Figure 112010019458035-pct00010
The maximum mutual information between and x k is:

Figure 112010019458035-pct00011

here

Figure 112010019458035-pct00012
Is the post-processing signal to interference and noise ratio (SINR) for element s of x k , and b k , s is
Figure 112010019458035-pct00013
S represents column s and t k , s represents column s of T k . The sum mutual information of all MU-MIMO systems is as follows.

Figure 112010019458035-pct00014

In the following, we will design a method of multi-user precoding and scheduling based on the channel model above. For clarity, a simpler situation of complete CSI at the transmitter will be introduced first, and then the invention will be described with respect to the situation of incomplete CSI.

complete CSI Multi-user by Precoding  And Scheduling

continuity MMSE (S- MMSE ) Multi-user Precoding

As mentioned above, in the present invention, due to the ability of the MMSE type multi-user precoding algorithm to balance interference and noise, where noise means both CCI and AWGN noise due to CSI error, May be used. In particular, a continuous MMSE (S-MMSE) algorithm is used, which is a simplified implementation of the MMSE type algorithm where each user has one or more receive antennas.

The basic principle of the MMSE type algorithm is to find a set of optimal precoding matrices { T k } and filtering matrices { B k } according to the MMSE criteria.

Figure 112010019458035-pct00015

Equation (6) relates to the joint optimization problem, the solution of which is generally very complicated to obtain. On the other hand, the S-MMSE algorithm provides a simplified difference-optimal solution to this problem by iterative operations:

Step 1 Initialize each B k by generating a random s k x N R matrix;

Step 2 Based on the current { B k }, the optimal precoding matrix { T k } is calculated according to the MMSE criterion as follows.

Figure 112010019458035-pct00016

here

Figure 112010019458035-pct00017

Figure 112010019458035-pct00018

And

Figure 112010019458035-pct00019

Step 3 Based on the { T k } calculated above, the filtering matrix { B k } is updated as follows according to the MMSE criterion.

Figure 112010019458035-pct00020

Steps 2 and 3 are carried out until the Frobenius norm of the change in steps { T k } and { B k } falls below a preset threshold or until the number of iterations reaches some value. Repeat.

Step 5 The final precoding matrix { T k }

Figure 112010019458035-pct00021
Normalized by

Multi-user Scheduling

The total number of users in a communication system is denoted by N. The scheduler selects some of the users among the N users for multi-user transmission, and also determines the number of data streams for each selected user. Possible scheduling results

Figure 112010019458035-pct00022
Notation,
Figure 112010019458035-pct00023
Is the three parts, the number of selected users K (
Figure 112010019458035-pct00024
), A set of indices for selected users
Figure 112010019458035-pct00025
,
Figure 112010019458035-pct00026
And the numbers of data streams for the selected users
Figure 112010019458035-pct00027
Figure 112010019458035-pct00028
It can be expressed as. The scheduler is a set of
Figure 112010019458035-pct00029
To explore and based on what criteria
Figure 112010019458035-pct00030
Choose the best ones marked with. For example, according to the capacity-maximization criteria, the scheduler
Figure 112010019458035-pct00031
.

Figure 112010019458035-pct00032

Figure 112010019458035-pct00033
Has a scheduler
Figure 112010019458035-pct00034
Is a set of K's to search for,
Figure 112010019458035-pct00035
silver
Figure 112010019458035-pct00036
And
Figure 112010019458035-pct00037
Represent each s-th column,
Figure 112010019458035-pct00038
And
Figure 112010019458035-pct00039
The
Figure 112010019458035-pct00040
Are the filtering and precoding matrices for the k th user. From here,
Figure 112010019458035-pct00041
And
Figure 112010019458035-pct00042
The
Figure 112010019458035-pct00043
And
Figure 112010019458035-pct00044
It is obtained through steps 1 to 5 above.
Figure 112010019458035-pct00045
The size of depends on the scheduling strategy used. For example, in full search scheduling,
Figure 112010019458035-pct00046
Is all
Figure 112010019458035-pct00047
Include the possibilities of

First

Figure 112013023965418-pct00048
Once selected, the data streams
Figure 112013023965418-pct00049
Number is precoding matrices
Figure 112013023965418-pct00050
With the same time-frequency resources by multi-user precoding
Figure 112013023965418-pct00051
Is sent to a number of users.

Incomplete according to the invention CSI By multi-user Precoding  And Scheduling

Incomplete channel matrix of user k available in BS

Figure 112010019458035-pct00052
And the CSI error for user k
Figure 112010019458035-pct00053
.
Figure 112010019458035-pct00054
In the elements, the mean is zero and the variance
Figure 112010019458035-pct00055
Suppose it is random variables of i i.
Figure 112010019458035-pct00056
Can be due to various factors, such as feedback quantization, feedback delay, and the like.
Figure 112010019458035-pct00057
It is also assumed that the value of is available at the transmitter.
Figure 112010019458035-pct00058
The value of can be obtained by various measurements such as measurement of channel estimation error, feedback error, quantization error, etc., and can be fed back to the transmitter at the receiver.

CCI - Estimate  Use S- MMSE Precoding

For incomplete CSI, the channel model in equation (3) is modified by the following equation.

Figure 112010019458035-pct00059

For optimization of the filtering matrices { B k } with a given { T k }, equation (13) can be rewritten as follows.

Figure 112010019458035-pct00060

here

Figure 112010019458035-pct00061
Is the term of CCI + noise including both CCI and AWGN due to CSI error.
Figure 112010019458035-pct00062
By approximating V as a vector of complex Gaussian noise, an optimal { B k } can be generated as follows.

Figure 112010019458035-pct00063

here,

Figure 112010019458035-pct00064

In equation (16), (a) is the

Figure 112010019458035-pct00065
To
Figure 112010019458035-pct00066
Derived by approximation as
Figure 112010019458035-pct00067
And
Figure 112010019458035-pct00068
Is the jth column of matrix A,
Figure 112010019458035-pct00069
Represents the Kronecker product,
Figure 112010019458035-pct00070
Is a covariance matrix of CSI errors
Figure 112010019458035-pct00071
to be.

For optimization of the precoding matrices { T k } with a given { B k }, equation (13) can be rewritten in a compact form as follows.

Figure 112010019458035-pct00072

here

Figure 112010019458035-pct00073

Figure 112010019458035-pct00074

Figure 112010019458035-pct00075

And B is defined in equation (9). Similarly,

Figure 112010019458035-pct00076
Is the term of CCI + noise including both CCI and AWGN due to CSI error.
Figure 112010019458035-pct00077
By approximating V as a vector of complex Gaussian noise, an optimal { T k } can be generated as follows:

Figure 112010019458035-pct00078

here

Figure 112010019458035-pct00079

In equation (25), (a) is

Figure 112010019458035-pct00080
of
Figure 112010019458035-pct00081
Derived by approximation as
Figure 112010019458035-pct00082
The
Figure 112010019458035-pct00083
Is the covariance matrix of, which is the covariance matrix of the CSI error.
Figure 112010019458035-pct00084
Can be calculated from

By replacing equations (7) and (11) with equations (15) and (24) in steps 1 to 5 above, the CCI estimate-based S-MMSE precoding algorithm of the present invention can be obtained.

CCI - Estimate  Multi-user Scheduling

In equation (13), the channel model can be rewritten for incomplete CSI as follows.

Figure 112010019458035-pct00085

Each user / mode subset

Figure 112010019458035-pct00086
For the scheduler, given by
Figure 112010019458035-pct00087
Estimate the achievable capacity by approximating V as a vector of complex Gaussian noise.

Figure 112010019458035-pct00088

From here

Figure 112013023965418-pct00089
And
Figure 112013023965418-pct00090
Can be calculated by equation (16). Subsequently, the CCI-estimated scheduling algorithm is based on the following criteria:
Figure 112013023965418-pct00091
.

Figure 112010019458035-pct00092

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings.

1 is a flowchart of a method of multi-user precoding and scheduling in accordance with the present invention.

In accordance with the present invention, in step 101, statistical characteristics of CSI (which may not be accurate due to various factors such as feedback quantization, feedback delay) and CSI errors are fed back from the UE to the BS. CSI is an estimate of the channel matrix and the statistical features of the CSI error are statistical features of the error matrix of the estimate of the channel matrix. For example, as shown above, the estimate of the channel matrix is

Figure 112013023965418-pct00093
And the statistical characteristics of the CSI error are covariance matrix
Figure 112013023965418-pct00094
Lt; / RTI > In step 103, the multi-user precoding matrix and scheduling scheme are generated by the BS, in accordance with the statistical characteristics of the feedback CSI and CSI error. As illustrated above, multi-user precoding may use an MMSE type algorithm, and multi-user scheduling may use capacity-maximization criteria. Finally, in step 105, user data is multi-user precoded and scheduled by using the generated multi-user precoding matrix and scheduling scheme.

2 is a block diagram of a BS for implementing the method of multi-user precoding and scheduling in accordance with the present invention. As shown in FIG. 2, the BS includes a receiving device 201, a multi-user precoding matrix and scheduling scheme generation device 203, and a multi-user precoding and scheduling device 205. The receiving device 201 receives statistical characteristics of CSI and CSI error fed back from the UE. The multi-user precoding matrix and scheduling scheme generation device 203 generates the multi-user precoding matrix and scheduling scheme in accordance with the statistical characteristics of the fed back CSI and CSI errors. Multi-user precoding and scheduling device 205 performs multi-user precoding and scheduling on user data by using the generated multi-user precoding matrix and scheduling scheme.

3 and 4 are graphs showing a performance comparison between the method according to the invention and the prior art method. As shown in Figs. 3 and 4, the CCI-estimated MU-MIMO scheme according to the present invention is a conventional MU- based on original S-MMSE and MET algorithms by a greedy scheduling strategy. Compared with the MIMO scheme. Here, four transmit antennas are arranged at the base station, two receive antennas per user, and the total number of users is four. The elements in the channel matrices {H k } are modeled as complex white Gaussian variables of iid with zero mean and unit variance. CSI error matrices

Figure 112013023965418-pct00095
Elements are zero mean and variance
Figure 112013023965418-pct00096
It is modeled as complex white Gaussian noises of i i d. In the present invention,
Figure 112013023965418-pct00097
Is set to 0.1 and 0.5 in FIGS. 3 and 4, respectively. Both MMSE and non-MMSE receivers are contemplated. As can be seen, the method proposed in the present invention outperforms MET and S-MMSE by greedy scheduling, for both MMSE and non-MMSE receivers, especially when the CSI error is large.

The present invention has the following advantages.

1. When incomplete CSI is used at the transmitter (this is actually the case), it greatly improves the system performance of the MU-MIMO.

2. Brings only the lowest additional complexity in BSs and no additional complexity in UEs.

3. It is adaptive with respect to the causes of CSI error, such as channel estimation error, quantization error, feedback error, etc. and can be used for various MU-MIMO mechanisms based on sounding and feedback, for example.

In summary, MU-MIMO operation is a hot topic in many broadband wireless communication standards such as IEEE 802.16 and 3GPP LTE due to its great potential for improving cell throughput. CSI error in the transmitter is one of the real problems that constrains the application of MU-MIMO in real systems. The solution provided by the present invention can bring obvious advantages at the expense of minimal additional complexity in the BS.

The foregoing is only preferred embodiments of the present invention, and the present invention is not limited to the above-described embodiments. Therefore, any modifications, substitutions and improvements to the present invention are possible without departing from the spirit and scope of the present invention.

201: receiving device
203: multi-user precoding matrix and scheduling scheme generation device
205: Multi-User Precoding and Scheduling Device

Claims (16)

  1. In the multi-user precoding and scheduling method,
    Feeding back channel state information (CSI) and statistical characteristics of the CSI error from the user equipment (UE) to the base station (BS);
    Generating, at the BS, a multi-user precoding matrix and scheduling scheme in accordance with the statistical characteristics of the fed back CSI and CSI error; And
    Performing multi-user precoding and scheduling on user data by using the generated multi-user precoding matrix and scheduling scheme.
  2. The method according to claim 1,
    Wherein the CSI is an estimate of a channel matrix.
  3. 3. The method of claim 2,
    And the statistical characteristics of the CSI error include a covariance matrix of the error of the estimate of the channel matrix.
  4. The method according to claim 1,
    Wherein the multi-user precoding uses a minimum mean square error (MMSE) type algorithm.
  5. 5. The method of claim 4,
    And the MMSE type algorithm is a continuous MMSE algorithm.
  6. The method according to claim 1,
    The multi-user scheduling uses capacity maximization criteria.
  7. The method according to claim 1,
    The statistical characteristics of the CSI error are obtained by measuring channel estimate error, feedback error, and quantization error, and then fed back from the receiver to the transmitter.
  8. The method according to claim 1,
    The method is used in a multi-user multiple input multiple output (MU-MIMO) communication system.
  9. In the base station,
    A receiving device receiving CSI and statistical characteristics of a CSI error fed back from a user equipment (UE);
    A multi-user precoding matrix and scheduling scheme generation device for generating a multi-user precoding matrix and a scheduling scheme in accordance with the fed back CSI and statistical characteristics of the CSI error; And
    And a multi-user precoding and scheduling device for performing multi-user precoding and scheduling on user data by using the generated multi-user precoding matrix and scheduling scheme.
  10. The method of claim 9,
    Wherein the CSI is an estimate of a channel matrix.
  11. 11. The method of claim 10,
    And statistical characteristics of the CSI error include a covariance matrix of the error of the estimate of the channel matrix.
  12. The method of claim 9,
    The multi-user precoding uses a minimum mean square error (MMSE) type algorithm.
  13. 13. The method of claim 12,
    And the MMSE type algorithm is a continuous MMSE algorithm.
  14. The method of claim 9,
    The multi-user scheduling uses capacity-maximization criteria.
  15. The method of claim 9,
    The statistical characteristics of the CSI error are obtained by measuring channel estimate error, feedback error and quantization error, and then fed back from the receiver to the transmitter.
  16. The method of claim 9,
    The base station is used in a multi-user multiple input multiple output (MU-MIMO) communication system.
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